Neuroelectronic interfacing with cultured multielectrode arrays toward a cultured probe

Efficient and selective electrical stimulation and recording of neural activity in peripheral, spinal, or central pathways requires multielectrode arrays at micrometer scale. ¿Cultured probe¿ devices are being developed, i.e., cell-cultured planar multielectrode arrays (MEAs). They may enhance efficiency and selectivity because neural cells have been grown over and around each electrode site as electrode-specific local networks. If, after implantation, collateral sprouts branch from a motor fiber (ventral horn area) and if they can be guided and contacted to each ¿host¿ network, a very selective and efficient interface will result. Four basic aspects of the design and development of a cultured probe, coated with rat cortical or dorsal root ganglion neurons, are described. First, the importance of optimization of the cell-electrode contact is presented. It turns out that impedance spectroscopy, and detailed modeling of the electrode-cell interface, is a very helpful technique, which shows whether a cell is covering an electrode and how strong the sealing is. Second, the dielectrophoretic trapping method directs cells efficiently to desired spots on the substrate, and cells remain viable after the treatment. The number of cells trapped is dependent on the electric field parameters and the occurrence of a secondary force, a fluid flow (as a result of field-induced heating). It was found that the viability of trapped cortical cells was not influenced by the electric field. Third, cells must adhere to the surface of the substrate and form networks, which are locally confined, to one electrode site. For that, chemical modification of the substrate and electrode areas with various coatings, such as polyethyleneimine (PEI) and fluorocarbon monolayers promotes or inhibits adhesion of cells. Finally, it is shown how PEI patterning, by a stamping technique, successfully guides outgrowth of collaterals from a neonatal rat lumbar spinal cord explant, after six days in cultur

Three-dimensional neuroelectronic interface for peripheral nerve stimulation and recording: realization steps and contacting technology

A three-dimensional array of microelectrodes for use in intraneural stimulation and recording is presented. The 128 electrodes are at the tips of silicon needles, which are electrically insulated from each other. The needles in the array have differing heights, resulting in a true three-dimensional electrode structure. The distance between the needles is 120 ¿m, while the heights are 600, 425 and 250 ¿m. An overview of the technology for the realization of the device is given, and the contacting of the array is discussed. The array is connected to a gate array (containing multiplexing electronics, current sources and buffer amplifiers) through controlled collapse chip connection

Molecular Layer Functionalized Neuroelectronic Interfaces: From Sub-Nanometer Molecular Surface Functionalization to Improved Mechanical and Electronic Cell-Chip Coupling

The interface between electronic components and biological objects plays a crucial role for the success of bioelectronic devices. Since the electronics typically include different elements such as an insulating substrate in combination with conducting electrodes, an important issue of bioelectronics involves tailoring and optimizing the interface for any envisioned application. In this work, we present a method of functionalizing insulating substrates (SiO2) and metallic electrodes (Pt) simultaneously with a stable monolayer of organic molecules ((3-aminopropyl)triethoxysilane (APTES)). This monolayer is characterized by various techniques like atomic force microscope (AFM), ellipsometry, time-of-flight secondary ion mass spectrometry (ToF-SIMS), surface plasmon resonance (SPR), and streaming potential measurements. The molecule layers of APTES on both substrates, Pt and SiO2, show a high molecule density, a coverage of ~ 50 %, a long-term stability (at least one year), a positive surface net charge, and the characteristics of a self-assembled monolayer (SAM). In the electronical characterization of the functionalized Pt electrodes via impedance spectroscopy measurements, the static properties of the electronic double layer could be separated from the diffusive part using a specially developed model. It could be demonstrated that compared to cleaned Pt electrodes the double layer capacitance is increased by an APTES coating and the charge transfer resistance is reduced, which leads to a total increase of the electronic signal transfer of ~13 %. In the final cell culture measurements, it could be shown that an APTES coating facilitates a conversion of bio-unfriendly Pt surfaces into biocompatible surfaces which allows cell growth (neurons) on both functionalized components (SiO2 and Pt) comparable to that of reference samples coated with poly-L-lysine. Furthermore, APTES coating leads to an improved mechanical coupling, which increases the sealing resistance and reduces losses. These increases were finally confirmed by electronic measurements on neurons, which showed action potential signals in the mV regime compared to signals of typically 200 – 400 µV obtained for reference measurements on PLL coated samples. Therefore, the functionalization with APTES molecules seems to be able to greatly improve the electronic cell-chip coupling (here by ~1 500 %). This significant increase of the electronic and mechanical cell-chip coupling might represent an important step for the improvement of neuroelectronic sensor and actuator devices

Intraneural stimulation using 2D wire-microelectrode arrays: I. Experimental results

A two-dimensional 24-channel wire-microelectrode array was inserted into the peroneal nerve of the rat during acute experiments. The electrodes in the array are on a regular grid of 6 by 4 electrodes; inter-electrode spacing is 120 ¿m. For each of the electrodes in the array the corresponding twitch-force recruitment curve was recorded from the extensor digitorum longus muscle (EDL). A complete set of 24 recruitment curves is presented The shape of the recruitment curves varies among the electrodes in the array. This supports previous findings which suggest a different motor unit recruitment order for stimulating electrodes at different intraneural position

Neuroelectronic interface. Of first experiments to the present days

Neuroelectronic interface - is a system designed to exchange information between the brain and an electronic device. Such an interface can give unlimited possibilities in the processing of information by a person. He is able to completely change the life of a person and greatly improve it. It is worth paying more attention to this direction

Development of a solderbump technique for contacting a three-dimensional multi electrode array

The application of a solder bump technique for contacting a multi electrode sensor/actuator system is presented. Techniques adapted from the literature could successfully be scaled down to 55×55 ¿m bumps at 120 ¿m heart-to-heart spacin

Characterizing Self-Developing Biological Neural Networks: A First Step Towards their Application To Computing Systems

Carbon nanotubes are often seen as the only alternative technology to silicon transistors. While they are the most likely short-term one, other longer-term alternatives should be studied as well. While contemplating biological neurons as an alternative component may seem preposterous at first sight, significant recent progress in CMOS-neuron interface suggests this direction may not be unrealistic; moreover, biological neurons are known to self-assemble into very large networks capable of complex information processing tasks, something that has yet to be achieved with other emerging technologies. The first step to designing computing systems on top of biological neurons is to build an abstract model of self-assembled biological neural networks, much like computer architects manipulate abstract models of transistors and circuits. In this article, we propose a first model of the structure of biological neural networks. We provide empirical evidence that this model matches the biological neural networks found in living organisms, and exhibits the small-world graph structure properties commonly found in many large and self-organized systems, including biological neural networks. More importantly, we extract the simple local rules and characteristics governing the growth of such networks, enabling the development of potentially large but realistic biological neural networks, as would be needed for complex information processing/computing tasks. Based on this model, future work will be targeted to understanding the evolution and learning properties of such networks, and how they can be used to build computing systems

Nonlinear Dynamic Modeling, Simulation And Characterization Of The Mesoscale Neuron-electrode Interface

Extracellular neuroelectronic interfacing has important applications in the fields of neural prosthetics, biological computation and whole-cell biosensing for drug screening and toxin detection. While the field of neuroelectronic interfacing holds great promise, the recording of high-fidelity signals from extracellular devices has long suffered from the problem of low signal-to-noise ratios and changes in signal shapes due to the presence of highly dispersive dielectric medium in the neuron-microelectrode cleft. This has made it difficult to correlate the extracellularly recorded signals with the intracellular signals recorded using conventional patch-clamp electrophysiology. For bringing about an improvement in the signalto-noise ratio of the signals recorded on the extracellular microelectrodes and to explore strategies for engineering the neuron-electrode interface there exists a need to model, simulate and characterize the cell-sensor interface to better understand the mechanism of signal transduction across the interface. Efforts to date for modeling the neuron-electrode interface have primarily focused on the use of point or area contact linear equivalent circuit models for a description of the interface with an assumption of passive linearity for the dynamics of the interfacial medium in the cell-electrode cleft. In this dissertation, results are presented from a nonlinear dynamic characterization of the neuroelectronic junction based on Volterra-Wiener modeling which showed that the process of signal transduction at the interface may have nonlinear contributions from the interfacial medium. An optimization based study of linear equivalent circuit models for representing signals recorded at the neuron-electrode interface subsequently iv proved conclusively that the process of signal transduction across the interface is indeed nonlinear. Following this a theoretical framework for the extraction of the complex nonlinear material parameters of the interfacial medium like the dielectric permittivity, conductivity and diffusivity tensors based on dynamic nonlinear Volterra-Wiener modeling was developed. Within this framework, the use of Gaussian bandlimited white noise for nonlinear impedance spectroscopy was shown to offer considerable advantages over the use of sinusoidal inputs for nonlinear harmonic analysis currently employed in impedance characterization of nonlinear electrochemical systems. Signal transduction at the neuron-microelectrode interface is mediated by the interfacial medium confined to a thin cleft with thickness on the scale of 20-110 nm giving rise to Knudsen numbers (ratio of mean free path to characteristic system length) in the range of 0.015 and 0.003 for ionic electrodiffusion. At these Knudsen numbers, the continuum assumptions made in the use of Poisson-Nernst-Planck system of equations for modeling ionic electrodiffusion are not valid. Therefore, a lattice Boltzmann method (LBM) based multiphysics solver suitable for modeling ionic electrodiffusion at the mesoscale neuron-microelectrode interface was developed. Additionally, a molecular speed dependent relaxation time was proposed for use in the lattice Boltzmann equation. Such a relaxation time holds promise for enhancing the numerical stability of lattice Boltzmann algorithms as it helped recover a physically correct description of microscopic phenomena related to particle collisions governed by their local density on the lattice. Next, using this multiphysics solver simulations were carried out for the charge relaxation dynamics of an electrolytic nanocapacitor with the intention of ultimately employing it for a simulation of the capacitive coupling between the neuron and the v planar microelectrode on a microelectrode array (MEA). Simulations of the charge relaxation dynamics for a step potential applied at t = 0 to the capacitor electrodes were carried out for varying conditions of electric double layer (EDL) overlap, solvent viscosity, electrode spacing and ratio of cation to anion diffusivity. For a large EDL overlap, an anomalous plasma-like collective behavior of oscillating ions at a frequency much lower than the plasma frequency of the electrolyte was observed and as such it appears to be purely an effect of nanoscale confinement. Results from these simulations are then discussed in the context of the dynamics of the interfacial medium in the neuron-microelectrode cleft. In conclusion, a synergistic approach to engineering the neuron-microelectrode interface is outlined through a use of the nonlinear dynamic modeling, simulation and characterization tools developed as part of this dissertation research